Scientist in advanced diagnostics of cyber-physical systems

Website ESI (TNO)

Improving high-tech systems availability by applying systematic methods to ensure their diagnosability and prognosability, from field introduction onwards. Can you envison it? Contribute to the world of tomorrow as a scientist at TNO ESI in Eindhoven.

What will be your role?
You will have the unique opportunity to make an impact in increasing complex cyber-physical systems (CPS) availability through fundamental improvements in diagnostics methods. You will contribute to the development, improvement and extension of these methods to make systems better diagnosable by design and during operation, improving both high tech systems up-time and product quality performance.
As a scientist operating from industry labs at high-tech giants, you bridge the gap between academia and industrial practice in applied research projects. You share insights that you gain during your research with industrial partners and science. As you often work in a small team, you have an important say in the course of the project. We look forward to you sharing your ideas.
The research projects from ESI (TNO) you can contribute to are numerous. ESI currently has projects on system diagnosability at ASML, Canon, and Philips, focusing both on diagnosing hardware problems and on software issues.

What we expect from you
You are a goal-oriented and self-driven professional energised by delivering high quality results. You enjoy working in a multidisciplinary team of ESI research fellows, industrial and academic partners, where you closely cooperate with the client’s team at the customer premises. Above all you are a team player that we can build on to guide our customer and our own staff successfully through its journey, and are as well able to independently work on problem solutions, motivated to integrate your work in the team result.

What else can you offer?
PhD in computer science, engineering or relevant field
Five or more years of experience, preferably in applied research. Experience with conceptual modelling or first principles modelling. Familiarity or experience with maintenance strategies, such as condition-based maintenance, or predictive maintenance. Interest or experience in diagnostics techniques such as FMEA, FTA, model-based diagnostics, and root cause analysis.
Experience with at least one and willingness to learn the others:

  • probabilistic reasoning techniques
  • knowledge graphs
  • graph theory
  • state machines or Petri nets

Hands-on programming experience, preferably Python or Java. Knowledge about DSLs is preferred.

A system-level mindset with great attention to detail. Outstanding communication and presentation abilities.

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